# python detect_mask_image.py --image examples/example_01.png
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
import numpy as np
import argparse
import cv2
import os

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", type=str,
	default="1.jpg",
	help="path to input image")
ap.add_argument("-f", "--face", type=str,
	default="face_detector",
	help="path to face detector model directory")
ap.add_argument("-m", "--model", type=str,
	default="mask_detector.model",
	help="path to trained face mask detector model")
ap.add_argument("-c", "--confidence", type=float, default=0.4,
	help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

# load our serialized face detector model from disk
print("[INFO] loading face detector model...")
prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])  #加载人脸检测器，OpenCV实现的SSD人脸检测器，基于深度学习框架Caffe训练的模型
weightsPath = os.path.sep.join([args["face"],
	"res10_300x300_ssd_iter_140000.caffemodel"])   #加载人脸检测器的权重
net = cv2.dnn.readNet(prototxtPath, weightsPath)

# load the face mask detector model from disk
print("[INFO] loading face mask detector model...")
model = load_model(args["model"])  #进行口罩检测的模型；先进性人脸的检测在进行口罩的检测

# load the input image from disk, clone it, and grab the image spatial
# dimensions
image = cv2.imread(args["image"])
orig = image.copy()
(h, w) = image.shape[:2]

# construct a blob from the image
blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300),  #图片归一化处理
	(104.0, 177.0, 123.0))

# pass the blob through the network and obtain the face detections
print("[INFO] computing face detections...")
net.setInput(blob)
detections = net.forward()
# 有几个可疑目标，detections 中的第三个list就存储了多少个list。
# 最后的每个list中存储的数据，从第二位起分别是:
# 可疑目标的标签，可疑目标的置信度，最后4个是可疑目标在图片中的位置信息。所以
# idx = int(detections[0, 0, 1, 1]) 提取第二个可疑目标的标签
# confidence = detections[0, 0, 1, 2] 提取第二个可疑目标的置信度
# box = detections[0, 0, 1, 3:7]提取第二个可疑目标的位置信息
for i in range(0, detections.shape[2]): #人脸的个数
	# extract the confidence (i.e., probability) associated with
	# the detection
	confidence = detections[0, 0, i, 2]  #取出置信度

	if confidence > args["confidence"]:

		box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) #进行原始图片的恢复
		(startX, startY, endX, endY) = box.astype("int")   #进行转化为整数型

		# ensure the bounding boxes fall within the dimensions of
		# the frame
		(startX, startY) = (max(0, startX), max(0, startY))  #确保框在图片范围之内
		(endX, endY) = (min(w - 1, endX), min(h - 1, endY))

		# extract the face ROI, convert it from BGR to RGB channel
		# ordering, resize it to 224x224, and preprocess it
		face = image[startY:endY, startX:endX]
		face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
		face = cv2.resize(face, (224, 224))
		face = img_to_array(face)
		face = preprocess_input(face)
		#print(face.shape)
		face = np.expand_dims(face, axis=0)  #进行维度的扩充
		print(model.predict(face)[0])
		(mask, withoutMask) = model.predict(face)[0]
		# determine the class label and color we'll use to draw
		# the bounding box and text
		label = "Mask" if mask > withoutMask else "No Mask"
		color = (0, 255, 0) if label == "Mask" else (0, 0, 255)  #口罩与不戴口罩人脸颜色的区分

		# include the probability in the label
		label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100) #进行百分数的输出

		# display the label and bounding box rectangle on the output
		# frame
		cv2.putText(image, label, (startX, startY - 10),
			cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
		cv2.rectangle(image, (startX, startY), (endX, endY), color, 2)

# show the output image
cv2.imshow("Output", image)
cv2.waitKey(0)